延迟焦化装置水力除焦系统设备故障与对策

中国石油大庆石化分公司炼油厂延迟焦化装置自2008年开工以来,水力除焦系统经常发生下钻困难、自动底盖机开关盖困难等故障。通过这部分的设备结构进行技术改进,其中包括采用新型支点轴承结构、更换钢丝绳类型、对自动底盖机进行改造等等。这些改进措施的实施,降低了设备的故障率,保证了装置的安全平稳运行。     

水力除焦控制系统在横河CENTUM-CS3000系统上的开发及应用

本文详细介绍了中国石化济南分公司50万吨/年延迟焦化装置水力除焦控制系统在日本横河CENTUM-CS3000系统上的开发过程,介绍了控制系统的构成和完成的功能,以及应用的效果.     

水力除焦控制系统在横河CENTUM-CS3000系统上的开发及应用

本文详细介绍了中国石化济南分公司50万吨/年延迟焦化装置水力除焦控制系统在日本横河CENTUM-CS3000系统上的开发过程,介绍了控制系统的构成和完成的功能,以及应用的效果。     

大口径平行光管实时检焦系统

大口径平行光管作为实验室测试和标定必不可少的设备,已经普遍应用于各种光学设备的标定与检测中。但是光管因为震动、温度的梯度变化和空气扰动等影响因素出现离焦现象时,会导致平行光管出射光发散角增大,测量结果可信度降低。针对以上问题,本文提出了一种利用五棱镜作为光反馈元件的小型焦面监测装置,采用CCD作为图像采集装置,利用软件实现系统离焦量的计算,可对大口径平行光管的焦面进行长时间高精度的监测。经过计算机仿真与试验表明:本系统检焦分辨力可以达到45μm以内,满足实验室检测设备精度要求     

基于LabVIEW力与变形信号数据自动采集与检测

以LabVIEW软件为平台,设计了力与变形信号数据自动采集系统,该装置可以实现在微机上动态显示、处理、存储力与变形信号数据.并以低碳钢拉伸实验为例,对拉伸过程中的应力应变进行了测试,从而将传统的拉伸测试转化成微机测试,这对传统力与变形信号数据自动采集与检测有着非常积极的意义.     

标准表法明渠流量计在线校准装置的设计

为解决现场评定废水在线自动监测系统中明渠流量计准确度的技术难题,设计了一套以高精度管道式流量计作为标准表的标准表法明渠流量计在线校准装置,研制了专用的现场流量信号采集测量系统,进行了硬件电路设计,并采用Qt开发了配套的上位机操作软件。通过大量不同条件下的现场对比试验,验证了系统的可靠性和装置的有效性,为准确实现废水流量在线监测提供了技术基础。     

Zigbee技术在桥吊信号采集中的应用

为实现对现场桥吊的实时检测,本文设计了基于ZigBee无线通信技术的信号采集检测系统.通过传感器检测桥吊梁上的应变、温度等信号,应用CC2530模块来构造无线采集系统,并利用其内部电源监控电路检测电压.本文重点介绍采集节点的硬件设计及软件设计,实现了信号的采集与无线传输.     

基于DSP的给水泵运行状态在线监测系统

给水泵在运行过程中的振动信号及其特征信息对给水泵系统的状态监测有重要意义.给水泵运行状态在线监测系统以数字信号处理器DSP为核心,采用以振动监测为主、以过程量监测为辅的监测方法,通过对采集信号的频谱分析和数字处理,将其由USB高速接口送入上位机,由上位机的专家系统对给水泵进行状态判断和故障诊断.本文介绍了给水泵运行状态在线监测系统的工作原理,分析了有限冲击响应(FIR)数字滤波算法和快速傅立叶变换(FFT)算法的DSP实现方法,重点阐述了系统的总体设计、硬件设计和软件设计.     

振动监测系统的可调增益技术与实现

文章设计了一种具有可调增益功能的振动监测系统。首先从监测层和测量层两个方面提出了振动监测系统的整体框架,之后给出了硬件系统和软件系统的设计方法及软件实现代码,利用控制芯片和可调增益放大器,在监测系统中整合了一套可根据采集信号幅值大小调整增益的数据采集装置;利用模拟信号发生器对不加增益和设置可调增益之后的两种信号进行了对比分析,并在汽轮发电机组实际工况下进行了振动信号采集。结果表明,具备可调增益功能的振动监测系统可有效提高采集信号的信噪比,提高汽轮发电机组的振动监测能力。     

基于无线局域网的仓库叉车状态可视化监测诊断系统研究

根据叉车故障类型和具有移动性的特点,构建了基于无线局域网(WLAN)IEEE 802.11b标准的叉车运行状态采集与信号无线传输系统.通过安装在各叉车上的无线发射装置,把叉车状态信息经A/D转换后,打包发送到位于仓库指挥中心的无线接收装置和现场监测工作站,形成一个可移动的叉车在线状态测试装置和一点对多点的无线网络.该无线网络通过Internet与远程诊断中心互连,实现了仓库叉车状态可视化、远程诊断和状态评估.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Identification of feature set for effective tool condition monitoring by acoustic emission sensing

In tool condition monitoring systems, various features from suitably processed acoustic emission signals are utilized by researchers. However, not all of these features are equally informative in a specific monitoring system: certain features may correspond to noise, not information; others may be correlated or not relevant for the task to be realized. This study comprehensively takes all these known signal features and aims to identify the most effective set that can give robust and reliable identification of tool condition. In this paper, the aim is investigated through feature selection, in which automatic relevance determination (ARD) under a Bayesian framework and support vector machine (SVM) are coupled together to perform this task. In tool condition monitoring, this proposed method is able to identify the worst features according to their corresponding ARD parameters and delete them. Then the effectiveness of this pruning may be evaluated by a model validation. Finally, the effective feature set in the developed tool wear recognition system is obtained. The experimental results show that the AE feature set selected through this method is more effective and efficient to recognize tool status over various cutting conditions.     

区域环境电磁监测与信号识别系统方案研究

针对侦察活动中大量使用类似通信信号的伪信号和进行科学实验时各种信号存在的电磁泄漏问题,为满足电磁环境监测的实际需要,运用LabWindows/CVI设计一种新型的区域环境电磁监测与识别系统。系统通过最新的信号混合域分析方法,对一定范围内的区域进行监测,实现仪器控制、数据采集、系统校准、信号识别、结果处理与保存等功能,具有准确性、易用性、便携性及可扩展性,可以高效科学地完成电磁环境监测工作,具有一定的实用性和推广价值。     

基于似然比检验原理的机床切削颤振早期监测

根据似然比检验原理提出了一种新的机床切削颤振监测统计量，能识别切削过程中产生的信噪比为０．１５的微弱振动成份。文章还就颤振监测工作特性、颤振监测门限值设置等有关问题进行了分析讨论。颤振监测考证试验在一台数控车床上进行。     

截齿磨损程度的多特征信号融合识别研究

为了解决采煤机开采过程中截齿磨损程度在线监测和状态识别的工程难题,提出一种基于多特征信号融合的截齿磨损程度识别方法。搭建截齿磨损程度监测实验台,分别测试提取不同磨损程度截齿截割过程中的振动加速度信号、声发射信号、红外热像信号和电机电流信号,建立了截齿截割的多传感信号数据样本库;针对数据样本库中两相邻磨损状态截齿特征样本存在数据交集、系统识别精度低的问题,构建最小模糊度优化模型并计算各特征信号的最优模糊隶属度函数,获取特征样本最大隶属度。构建截齿磨损程度的神经网络识别模型,运用多特征数据样本对Back-Propagation（BP）神经网络进行学习和训练。实验结果表明：BP神经网络识别模型的识别结果和试样的实际磨损程度类别相同,此识别模型能够对截齿磨损程度类型进行实时监测和准确识别。研究结果为实际工程中截齿监测和更换提供了解决方案。     

岩石破裂电磁辐射监测系统研制

为满足有关岩石破裂电磁辐射研究的需要,研制一套电磁辐射监测系统。该系统由电磁感应探头、信号调理电路和数据采集系统组成,可对频率在1kHz~1MHz内的微弱电磁场进行监测。电磁感应探头可感应空间中某一方向的电磁场变化,并将其转换成电信号;信号调理电路对探头输出的电信号进行放大、滤波,并实现放大滤波电路与计算机的电气隔离;数据采集软件基于LabVIEW编写,用于对监测到的信号进行记录和显示。实验结果表明：该系统性能稳定,操作简便,能够有效地监测岩石破裂所产生的电磁辐射,对岩石、混凝土等材料的力-电-磁研究具有重要意义。     

远程测振系统在群楼拆除爆破振动监测中的应用

为了探索数字爆破测振系统在工程爆破危害效应实时监测中的应用效果,以汉口滨江商务区拆除爆破19栋群楼的工程为例,按照公安主管部门在审批拆除爆破方案时的要求,该工程必须采用控制爆破的方法,同时加强拆除爆破产生危害效应的实时监测。为此,本次群楼拆除爆破采用的是广州中爆数字公司研发的远程测振系统,对群楼拆除爆破过程中产生的爆破振动及塌落振动进行了全程、在线监测。通过远程测振系统,能够实现爆破测振数据的自动记录、远程传输和精细处理,进而推动爆破行业的快速发展,同时作为爆破安全监管的数字化、信息化手段,可对拆除爆破危害效应进行实时在线监测,可有效提升公安主管部门对复杂环境下爆破工程项目作业安全的远程监管能力。应用结果表明,远程测振系统可对爆破振动进行实时有效监测。     

一种监控非规整膜系的新方法

由透过率和波长的函数关系可知,膜系中每一层都可能存在若干波长的光信号会出现极值偏转。利用这些波长作为每层膜的监控波长,满足光电极值法的监控条件,由此提出了一种监控非规整膜系的新方法。这些波长可以通过波长搜索的方法而得出。从监控模拟和随机误差曲线可知,该方法继承了光电极值法简单易行以及自动补偿等优点,其反射率在 0.1%范围内变化,与理论曲线基本吻合。     

Tool condition monitoring by acoustic analysis through a solid path

A sensing method using an acoustic signal obtained in a relative low frequency range through a solid path for the monitoring of tool wear has been investigated. Such acoustic signals could be in the form of stress waves that are released during a machining process, which can be picked up by a regular ferroelectric microphone. Data analysis was conducted in both time and frequency domains. A clear pattern in such signals corresponding to the tool wear conditions has been identified. Several components in spectra were found in the pattern for indicating sudden changes of tool wear or breakage occurring at major cutting edges. It was also observed that the RMS and variance values of the signals could indicate the specific wear condition of the tool. Therefore, this kind of acoustic signal carries sensitive information about the progress of tool wear and can be implemented on line for monitoring tool wear.     

Automatic feature selection for unsupervised clustering of cycle-based signals in manufacturing processes

Recent developments in sensing and computer technology have resulted in most manufacturing processes becoming a data-rich environment. A cycle-based signal refers to an analog or digital signal that is obtained during each repetition of an operation cycle in a manufacturing process. It is a very important class of in-process sensing signals for manufacturing processes because it contains extensive information on the process condition and product quality (e.g., the forming force signal in forging processes). In contrast with currently available supervised classification approaches that heavily depend on the training dataset or engineering field knowledge, this paper aims to develop an automatic feature selection method for the unsupervised clustering of cycle-base signals. First, principal component analysis is applied to the raw signals. Then a new method is proposed to select information containing principal components to allow clustering to be performed. The dimension of the problem can be significantly reduced through the use of these two steps. Finally, a model-based clustering method is applied to the selected principal components to find the clusters in the cycle-based signals. A numerical example and a real-world example of a forging process are used to illustrate the effectiveness of the proposed method. The proposed technique is an important data pre-processing technique for the monitoring and diagnostic system development using cycle-based signals for manufacturing processes.